Characterization of cell-fate decision landscapes by estimating transcription factor dynamics

Abstract

AbstractModulation of gene expression during differentiation by transcription factors promotes cell diversity. Despite their role in cell fate decisions, no experimental assays estimate their regulatory activity in a high-throughput manner and at the single-cell resolution. We present FateCompass for identifying lineage-specific transcription factors across differentiation. It uses single-cell transcriptomics data to infer differentiation trajectories and transcription factor activities. We combined a probabilistic framework with RNA velocities or a differentiation potential to estimate transition probabilities and perform stochastic simulations. Also, we learned transcription factor activities using a linear model of gene regulation. Considering dynamic changes and correlations, we identified lineage-specific regulators. We applied FateCompass to an islet cell formation dataset from the mouse embryo, and we found known and novel potential cell-type drivers. Also, when applied to a differentiation protocol dataset towards beta-like cells, we pinpointed undescribed regulators of an off-target population, which were experimentally validated. Thus, as a framework for identifying lineage-specific transcription factors, FateCompass could have implications on hypothesis generation to increase the understanding of the gene regulatory networks driving cell fate choices.HighlightsWe developed FateCompass, a flexible pipeline to estimate transcription factor activities during cell-fate decision using single-cell RNA seq data.FateCompass outlines gene expression stochastic trajectories by infusing the direction of differentiation using RNA velocity or a differentiation potential when RNA velocity fails.Transcription factor dynamics allow the identification of time-specific regulatory interactions.FateCompass predictions revealed known and novel cell-subtype-specific regulators of mouse pancreatic islet cell development.Differential motif analysis predicts lineage-specific regulators of stem cell-derived human β- cells and sheds light on the cellular heterogeneity of β-cell differentiation protocols.Experimental validation supports the proposed GRN controlling SC-EC differentiation predicted by FateCompass.